{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T04:55:02Z","timestamp":1781758502020,"version":"3.54.5"},"reference-count":48,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"DOI":"10.1109\/ijcnn.2018.8489692","type":"proceedings-article","created":{"date-parts":[[2018,10,19]],"date-time":"2018-10-19T22:25:09Z","timestamp":1539987909000},"page":"1-8","source":"Crossref","is-referenced-by-count":9,"title":["A Short Review of Deep Learning Methods for Understanding Group and Crowd Activities"],"prefix":"10.1109","author":[{"given":"Luis Felipe","family":"Borja-Borja","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcelo","family":"Saval-Calvo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jorge","family":"Azorin-Lopez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","article-title":"A survey of recent advances in cnn-based single image crowd counting and density estimation","author":"sindagi","year":"2017","journal-title":"Pattern Recognition Letters"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS.2017.8078491"},{"key":"ref33","article-title":"Deep learning from crowds","author":"rodrigues","year":"2017","journal-title":"arXiv preprint arXiv 1709 04396"},{"key":"ref32","first-page":"833","article-title":"Contractive auto-encoders: Explicit invariance during feature extraction","author":"rifai","year":"2011","journal-title":"Proceedings of the 28th International Conference on Inter- national Conference on Machine Learning"},{"key":"ref31","article-title":"Plug-and-play cnn for crowd motion analysis: An application in abnormal event detection","author":"ravanbakhsh","year":"2016","journal-title":"arXiv preprint arXiv 1610 00307"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206641"},{"key":"ref37","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv preprint arXiv 1409 1556"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.453"},{"key":"ref35","first-page":"4657","article-title":"Deeply learned attributes for crowded scene understanding","author":"shao","year":"2015"},{"key":"ref34","article-title":"Recent advances in recurrent neural networks","author":"salehinejad","year":"2017","journal-title":"arXiv preprint arXiv 1801 01078"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/72.963769"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1177\/1356389017697615"},{"key":"ref40","first-page":"4278","article-title":"Inception-v4, inception-resnet and the impact of residual con- nections on learning","author":"szegedy","year":"2017","journal-title":"AAAI"},{"key":"ref12","article-title":"Neural turing machines","author":"graves","year":"2014","journal-title":"arXiv preprint arXiv 1410 5401"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref14","first-page":"630","article-title":"Identity mappings in deep residual networks","author":"he","year":"2016","journal-title":"European Conference on Computer Vision"},{"key":"ref15","first-page":"1971","author":"ibrahim","year":"2016","journal-title":"A hierarchical deep temporal model for group activity recognition"},{"key":"ref16","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"International Conference on Machine Learning"},{"key":"ref17","article-title":"Crowd counting by adapting convolutional neural networks with side information","author":"kang","year":"2016","journal-title":"arXiv preprint arXiv 1611 06748"},{"key":"ref18","article-title":"An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos","author":"ravi kiran","year":"2018","journal-title":"arXiv preprint arXiv 1801 04144"},{"key":"ref19","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems 25"},{"key":"ref4","first-page":"1282","article-title":"What are they doing?: Collective activity classification using spatio-temporal relationship among people","author":"choi","year":"2009"},{"key":"ref28","first-page":"52","article-title":"Stacked convolutional auto-encoders for hierarchical feature extraction","author":"masci","year":"2011","journal-title":"International Conference on Artificial Neural Networks"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/978-3-642-33765-9_16","article-title":"A unified framework for multi-target tracking and collective activity recognition","author":"choi","year":"2012","journal-title":"Computer Vision–ECCV 2012"},{"key":"ref27","article-title":"Resnetcrowd: A residual deep learning architecture for crowd counting, violent behaviour detection and crowd density level classification","author":"marsden","year":"2017","journal-title":"arXiv preprint arXiv 1705 10698"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298815"},{"key":"ref5","first-page":"3273","author":"choi","year":"2011","journal-title":"Learning context for collective activity recognition"},{"key":"ref29","article-title":"Recurrent neural networks","volume":"5","author":"medsker","year":"2001","journal-title":"Design and Applications"},{"key":"ref8","article-title":"Deep structured models for group activity recognition","author":"deng","year":"2015","journal-title":"arXiv preprint arXiv 1506 03857"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.516"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/2964284.2967300"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/MWSCAS.2017.8053243"},{"key":"ref1","author":"an","year":"2015","journal-title":"Variational autoencoder based anomaly detection using reconstruction probability"},{"key":"ref46","first-page":"1655","article-title":"Deep spatio-temporal residual networks for citywide crowd flows prediction","author":"zhang","year":"2017","journal-title":"AAAI"},{"key":"ref20","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Advances in neural information processing systems"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298684"},{"key":"ref48","first-page":"712","article-title":"Crossing-line crowd counting with two-phase deep neural networks","author":"zhao","year":"2016","journal-title":"European Conference on Computer Vision"},{"key":"ref22","author":"lebanoff","year":"2015","journal-title":"Counting in dense crowds using deep learning"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.70"},{"key":"ref21","first-page":"1216","article-title":"Beyond actions: Discriminative models for contextual group activities","author":"lan","year":"2010","journal-title":"Advances in neural information processing systems"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299143"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.2991\/amitp-16.2016.67"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref44","first-page":"818","article-title":"Visualizing and understanding convolutional networks","author":"zeiler","year":"2014","journal-title":"European Conference on Computer Vision"},{"key":"ref26","article-title":"Adversarial autoencoders","author":"makhzani","year":"2015","journal-title":"arXiv preprint arXiv 1511 05271"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.783"},{"key":"ref25","first-page":"1975","author":"mahadevan","year":"2010","journal-title":"Anomaly detection in crowded scenes"}],"event":{"name":"2018 International Joint Conference on Neural Networks (IJCNN)","location":"Rio de Janeiro","start":{"date-parts":[[2018,7,8]]},"end":{"date-parts":[[2018,7,13]]}},"container-title":["2018 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8465565\/8488986\/08489692.pdf?arnumber=8489692","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,23]],"date-time":"2020-08-23T23:32:15Z","timestamp":1598225535000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8489692\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":48,"URL":"https:\/\/doi.org\/10.1109\/ijcnn.2018.8489692","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}